Statistically robust detection and evaluation of errors in DTMs

Amirreza Saati; Hossein Arefi; Michael Schmitt; Uwe Stilla

doi:10.1109/JURSE.2011.5764780

Statistically robust detection and evaluation of errors in DTMs

Amirreza Saati, Hossein Arefi, Michael Schmitt, Uwe Stilla

Associate Professorship of Photogrammetry and Remote Sensing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Digital Terrain Models (DTMs) have been an important topic in the study of ground surface landform, therefore precise evaluation of errors in DTMs production is a critical factor to assess the quality of DTM. In this paper the attribute of errors in DTMs are characterized and robust statistical methods are proposed as accuracy measure. A method based on robust statistical estimation is presented to detect gross errors in DTMs. For practical example a region in Catalonia, Spain, including city areas (Terrassa) as well as forest steep mountainous terrain (La Mola) is selected to evaluate the performance of DTM generation algorithm and to analyze the significance of errors for World view-1 satellite images.

Original language	English
Title of host publication	2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings
Pages	305-308
Number of pages	4
DOIs	https://doi.org/10.1109/JURSE.2011.5764780
State	Published - 2011
Event	IEEE GRSS and ISPRS Joint Urban Remote Sensing Event, JURSE 2011 - Munich, Germany Duration: 11 Apr 2011 → 13 Apr 2011

Publication series

Name	2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings

Conference

Conference	IEEE GRSS and ISPRS Joint Urban Remote Sensing Event, JURSE 2011
Country/Territory	Germany
City	Munich
Period	11/04/11 → 13/04/11

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10.1109/JURSE.2011.5764780

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title = "Statistically robust detection and evaluation of errors in DTMs",

abstract = "Digital Terrain Models (DTMs) have been an important topic in the study of ground surface landform, therefore precise evaluation of errors in DTMs production is a critical factor to assess the quality of DTM. In this paper the attribute of errors in DTMs are characterized and robust statistical methods are proposed as accuracy measure. A method based on robust statistical estimation is presented to detect gross errors in DTMs. For practical example a region in Catalonia, Spain, including city areas (Terrassa) as well as forest steep mountainous terrain (La Mola) is selected to evaluate the performance of DTM generation algorithm and to analyze the significance of errors for World view-1 satellite images.",

author = "Amirreza Saati and Hossein Arefi and Michael Schmitt and Uwe Stilla",

year = "2011",

doi = "10.1109/JURSE.2011.5764780",

language = "English",

isbn = "9781424486571",

series = "2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings",

pages = "305--308",

booktitle = "2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings",

note = "IEEE GRSS and ISPRS Joint Urban Remote Sensing Event, JURSE 2011 ; Conference date: 11-04-2011 Through 13-04-2011",

}

Saati, A, Arefi, H, Schmitt, M & Stilla, U 2011, Statistically robust detection and evaluation of errors in DTMs. in 2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings., 5764780, 2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings, pp. 305-308, IEEE GRSS and ISPRS Joint Urban Remote Sensing Event, JURSE 2011, Munich, Germany, 11/04/11. https://doi.org/10.1109/JURSE.2011.5764780

Statistically robust detection and evaluation of errors in DTMs. / Saati, Amirreza; Arefi, Hossein; Schmitt, Michael et al.
2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings. 2011. p. 305-308 5764780 (2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Statistically robust detection and evaluation of errors in DTMs

AU - Saati, Amirreza

AU - Arefi, Hossein

AU - Schmitt, Michael

AU - Stilla, Uwe

PY - 2011

Y1 - 2011

N2 - Digital Terrain Models (DTMs) have been an important topic in the study of ground surface landform, therefore precise evaluation of errors in DTMs production is a critical factor to assess the quality of DTM. In this paper the attribute of errors in DTMs are characterized and robust statistical methods are proposed as accuracy measure. A method based on robust statistical estimation is presented to detect gross errors in DTMs. For practical example a region in Catalonia, Spain, including city areas (Terrassa) as well as forest steep mountainous terrain (La Mola) is selected to evaluate the performance of DTM generation algorithm and to analyze the significance of errors for World view-1 satellite images.

AB - Digital Terrain Models (DTMs) have been an important topic in the study of ground surface landform, therefore precise evaluation of errors in DTMs production is a critical factor to assess the quality of DTM. In this paper the attribute of errors in DTMs are characterized and robust statistical methods are proposed as accuracy measure. A method based on robust statistical estimation is presented to detect gross errors in DTMs. For practical example a region in Catalonia, Spain, including city areas (Terrassa) as well as forest steep mountainous terrain (La Mola) is selected to evaluate the performance of DTM generation algorithm and to analyze the significance of errors for World view-1 satellite images.

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U2 - 10.1109/JURSE.2011.5764780

DO - 10.1109/JURSE.2011.5764780

M3 - Conference contribution

AN - SCOPUS:79957643700

SN - 9781424486571

T3 - 2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings

SP - 305

EP - 308

BT - 2011 Joint Urban Remote Sensing Event, JURSE 2011 - Proceedings

T2 - IEEE GRSS and ISPRS Joint Urban Remote Sensing Event, JURSE 2011

Y2 - 11 April 2011 through 13 April 2011

ER -

Statistically robust detection and evaluation of errors in DTMs

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